Prediction of heart disease using data mining techniques

Chadha, Ritika; Mayank, Shubhankar

doi:10.1007/s40012-016-0121-0

Cited by 42 publications

(10 citation statements)

References 3 publications

(2 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…According to the construction of random forest, the fusion of multiple decision trees is conducted and the values of random vector are obtained independently based on each tree's original feature space with same distribution. In decision tree, the different features are selected to form the tree nodes and their importance are calculated using random forest (Ritika and Mayank, 2016). In feature selection, still the presence of some unrelated data leads poor accuracy for prediction process.…”

Section: Selection Of Featuresmentioning

confidence: 99%

An Analysis of Heart Disease for Diabetic Patients Using Recursive Feature Elimination with Random Forest

Channabasavaraju¹,

Vinayakamurthy²

2020

Journal of Computer Science

View full text Add to dashboard Cite

Data Mining Techniques are used in many areas like banking, healthcare, education because of extracting relevant information from the database. Many algorithms in data mining are used to predict the disease to reduce the patient treatment cost as well as increase the diagnosis accuracy. In this research work, the risk level of diabetic heart disease patient can be predicted using two datasets such as Pima Indians Diabetes dataset and heart disease dataset. Based on the World Health Organization (WHO), diabetes is the one of the biggest health concerns so mining the diabetes data is an ambiguous task. Diabetic patients may also have to suffer from other diseases like heart disease, eye complications, kidney disease, nerve damage, foot problems, skin complications and dental diseases. However, the existing techniques faced the difficulty to identify inconsistent and redundant features. In this research work, the Random Forest Feature Selection Technique (RFS) is developed to identify the significant features and eliminate irrelevant features which are used to improve the predicting accuracy of Cardio Vascular (CV) disease for diabetic patients. According to the output from the extensive experiments, this research categorizes that whether the diabetic patients having CV or not by using True Positive as diabetes and True Negative as no diabetes.

show abstract

Section: Selection Of Featuresmentioning

confidence: 99%

An Analysis of Heart Disease for Diabetic Patients Using Recursive Feature Elimination with Random Forest

Channabasavaraju¹,

Vinayakamurthy²

2020

Journal of Computer Science

View full text Add to dashboard Cite

show abstract

“…The heart patient's diagnosis can be assisted effectively by proposed machine-learning-based decision support system. Kumari and Godara et al [18] Chadha and Mayank [20] extracted interested patterns to predict the heart disease using data mining techniques. This paper strives to bring out the methodology and implementation of these techniques such as ANNs, DT and NBs and stress upon the results and conclusion induced on the basis of accuracy and time complexity.…”

Section: Related Workmentioning

confidence: 99%

A Critical Review on Medical Image Processing Techniques

2020

jcr

View full text Add to dashboard Cite

Medical image processing is highly a vital task in the field of health informatics. Various techniques are used to process the images captured using methods like Medical Resonance Imaging (MRI), Computed Tomography(CT), Positron Emission Tomography (PET) and the process includes, denoising, feature extraction, segmentation and classification. Different methods are implemented for every process. Process of eliminating noise from an acquired image is termed as denoising and various filters are designed for the same. Different features like local binary pattern, gray level similarity are extracted for segmentation process. Clustering algorithms like fuzzy-c-means, fuzzy-k-means algorithms are used for image segmentation. Particle Swarm Optimization (PSO), convolutional neural networks are also used for the purpose of image segmentation. Performance of these methods are compared using various performance evaluation metrics like, precision, recall, accuracy, sensitivity.

show abstract

“…Ritika Chadha et al [22] attempts to provide the methodology & implementation of the techniques for example Decision Tree, Artificial Neural Networks(ANN) & Naive Bayes. Certainly, the observations expose that Artificial ANN performs better than Decision Tree & Naive Bayes.…”

Section: Y = β2x + β1mentioning

confidence: 99%

A Survey on Risk Assessments of Heart Attack Using Data Mining Approaches

Solanki¹,

Sharma²

2019

IJIEEB

View full text Add to dashboard Cite

This document presents the required layout of articles to Medical data mining has become one of the prominent issues in the field of data mining due to the delicate lifestyle opted by the people which are leading them towards various chronicle health diseases. Heart disease is one of the conspicuous public health concern worldwide issues. Since clinical data is growing rapidly owing to deficient health awareness, various techniques and scientific methods are opted for analyzing this huge data. Several data mining techniques such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Decision tree, Naï ve Bayes and Artificial Neural Network (ANN) are introduced for the prediction of health disease. These techniques help to mine the relevant and useful amount of data, form the medical dataset which helps to provide beneficial information to the medical institutions. This study presents various issues related to healthcare and various machines learning algorithms which have to withstand to provide the best possible output. A comprehensive review of the literature has been summarized to put lights on the previous work done in this field.

show abstract

Prediction of heart disease using data mining techniques

Cited by 42 publications

References 3 publications

An Analysis of Heart Disease for Diabetic Patients Using Recursive Feature Elimination with Random Forest

An Analysis of Heart Disease for Diabetic Patients Using Recursive Feature Elimination with Random Forest

A Critical Review on Medical Image Processing Techniques

A Survey on Risk Assessments of Heart Attack Using Data Mining Approaches

Contact Info

Product

Resources

About